Abstract
Background: Placental volume measurements can potentially identify high-risk pregnancies. We aimed to develop and validate a new method for placental volume measurements using tracked 2D ultrasound and automatic image segmentation. Methods: We included 43 pregnancies at gestational week 27 and acquired placental images using a 2D ultrasound probe with position tracking, and trained a convolutional neural network (CNN) for automatic image segmentation. The automatically segmented 2D images were combined with tracking data to calculate placental volume. For 15 of the included pregnancies, placental volume was also estimated based on MRI examinations, 3D ultrasound and manually segmented 2D ultrasound images. The ultrasound methods were compared to MRI (gold standard). Results: The CNN demonstrated good performance in automatic image segmentation (F1-score 0.84). The correlation with MRI-based placental volume was similar for tracked 2D ultrasound using automatically segmented images (absolute agreement intraclass correlation coefficient [ICC] 0.58, 95% CI 0.13–0.84) and manually segmented images (ICC 0.59, 95% CI 0.13–0.84). The 3D ultrasound method showed lower ICC (0.35, 95% CI −0.11 to 0.74) than the methods based on tracked 2D ultrasound. Conclusions: Tracked 2D ultrasound with automatic image segmentation is a promising new method for placental volume measurements and has potential for further improvement.